2 research outputs found
Structuring User-Generated Content on Social Media with Multimodal Aspect-Based Sentiment Analysis
People post their opinions and experiences on social media, yielding rich
databases of end users' sentiments. This paper shows to what extent machine
learning can analyze and structure these databases. An automated data analysis
pipeline is deployed to provide insights into user-generated content for
researchers in other domains. First, the domain expert can select an image and
a term of interest. Then, the pipeline uses image retrieval to find all images
showing similar contents and applies aspect-based sentiment analysis to outline
users' opinions about the selected term. As part of an interdisciplinary
project between architecture and computer science researchers, an empirical
study of Hamburg's Elbphilharmonie was conveyed on 300 thousand posts from the
platform Flickr with the hashtag 'hamburg'. Image retrieval methods generated a
subset of slightly more than 1.5 thousand images displaying the
Elbphilharmonie. We found that these posts mainly convey a neutral or positive
sentiment towards it. With this pipeline, we suggest a new big data analysis
method that offers new insights into end-users opinions, e.g., for architecture
domain experts.Comment: 9 pages, 5 figures, short paper version to be published at 9th
IEEE/ACM International Conference on Big Data Computing, Applications and
Technologies (BDCAT2022
An Analysis of Programming Course Evaluations Before and After the Introduction of an Autograder
Commonly, introductory programming courses in higher education institutions
have hundreds of participating students eager to learn to program. The manual
effort for reviewing the submitted source code and for providing feedback can
no longer be managed. Manually reviewing the submitted homework can be
subjective and unfair, particularly if many tutors are responsible for grading.
Different autograders can help in this situation; however, there is a lack of
knowledge about how autograders can impact students' overall perception of
programming classes and teaching. This is relevant for course organizers and
institutions to keep their programming courses attractive while coping with
increasing students.
This paper studies the answers to the standardized university evaluation
questionnaires of multiple large-scale foundational computer science courses
which recently introduced autograding. The differences before and after this
intervention are analyzed. By incorporating additional observations, we
hypothesize how the autograder might have contributed to the significant
changes in the data, such as, improved interactions between tutors and
students, improved overall course quality, improved learning success, increased
time spent, and reduced difficulty. This qualitative study aims to provide
hypotheses for future research to define and conduct quantitative surveys and
data analysis. The autograder technology can be validated as a teaching method
to improve student satisfaction with programming courses.Comment: Accepted full paper article on IEEE ITHET 202